Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory383.8 KiB
Average record size in memory393.0 B

Variable types

DateTime1
Text2
Categorical2
Numeric6

Alerts

Timestamp has unique valuesUnique
Source IP has unique valuesUnique
Destination IP has unique valuesUnique

Reproduction

Analysis started2024-02-27 01:27:43.239281
Analysis finished2024-02-27 01:27:48.073053
Duration4.83 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Timestamp
Date

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2024-01-12 00:00:00
Maximum2024-02-22 15:00:00
2024-02-26T20:27:48.189975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:48.360874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Source IP
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.7 KiB
2024-02-26T20:27:48.608924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.194
Min length10

Characters and Unicode

Total characters13194
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row3.74.124.55
2nd row113.188.61.223
3rd row175.61.133.26
4th row216.23.15.20
5th row169.243.204.167
ValueCountFrequency (%)
3.74.124.55 1
 
0.1%
73.207.34.112 1
 
0.1%
82.160.225.3 1
 
0.1%
81.80.201.92 1
 
0.1%
175.61.133.26 1
 
0.1%
216.23.15.20 1
 
0.1%
169.243.204.167 1
 
0.1%
179.83.244.194 1
 
0.1%
63.245.16.96 1
 
0.1%
141.42.27.48 1
 
0.1%
Other values (990) 990
99.0%
2024-02-26T20:27:48.997689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.7%
1 2489
18.9%
2 1604
12.2%
3 864
 
6.5%
4 834
 
6.3%
5 785
 
5.9%
9 745
 
5.6%
8 742
 
5.6%
6 728
 
5.5%
0 702
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10194
77.3%
Other Punctuation 3000
 
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2489
24.4%
2 1604
15.7%
3 864
 
8.5%
4 834
 
8.2%
5 785
 
7.7%
9 745
 
7.3%
8 742
 
7.3%
6 728
 
7.1%
0 702
 
6.9%
7 701
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3000
22.7%
1 2489
18.9%
2 1604
12.2%
3 864
 
6.5%
4 834
 
6.3%
5 785
 
5.9%
9 745
 
5.6%
8 742
 
5.6%
6 728
 
5.5%
0 702
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3000
22.7%
1 2489
18.9%
2 1604
12.2%
3 864
 
6.5%
4 834
 
6.3%
5 785
 
5.9%
9 745
 
5.6%
8 742
 
5.6%
6 728
 
5.5%
0 702
 
5.3%

Destination IP
Text

UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size68.8 KiB
2024-02-26T20:27:49.220742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.276
Min length9

Characters and Unicode

Total characters13276
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row61.26.104.61
2nd row27.97.112.90
3rd row71.111.137.21
4th row10.79.132.72
5th row165.162.181.179
ValueCountFrequency (%)
61.26.104.61 1
 
0.1%
203.217.239.79 1
 
0.1%
168.39.129.73 1
 
0.1%
217.163.139.48 1
 
0.1%
71.111.137.21 1
 
0.1%
10.79.132.72 1
 
0.1%
165.162.181.179 1
 
0.1%
21.105.176.221 1
 
0.1%
117.17.79.208 1
 
0.1%
38.17.41.9 1
 
0.1%
Other values (990) 990
99.0%
2024-02-26T20:27:49.576908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3000
22.6%
1 2552
19.2%
2 1661
12.5%
3 855
 
6.4%
4 815
 
6.1%
5 810
 
6.1%
7 754
 
5.7%
9 713
 
5.4%
8 711
 
5.4%
0 707
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10276
77.4%
Other Punctuation 3000
 
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2552
24.8%
2 1661
16.2%
3 855
 
8.3%
4 815
 
7.9%
5 810
 
7.9%
7 754
 
7.3%
9 713
 
6.9%
8 711
 
6.9%
0 707
 
6.9%
6 698
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 3000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3000
22.6%
1 2552
19.2%
2 1661
12.5%
3 855
 
6.4%
4 815
 
6.1%
5 810
 
6.1%
7 754
 
5.7%
9 713
 
5.4%
8 711
 
5.4%
0 707
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3000
22.6%
1 2552
19.2%
2 1661
12.5%
3 855
 
6.4%
4 815
 
6.1%
5 810
 
6.1%
7 754
 
5.7%
9 713
 
5.4%
8 711
 
5.4%
0 707
 
5.3%

Attack Type
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size64.7 KiB
Phishing
179 
Ransomware
176 
Malware
172 
DDoS
170 
Insider Threat
153 

Length

Max length14
Median length10
Mean length9.168
Min length4

Characters and Unicode

Total characters9168
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMalware
2nd rowRansomware
3rd rowDDoS
4th rowMalware
5th rowRansomware

Common Values

ValueCountFrequency (%)
Phishing 179
17.9%
Ransomware 176
17.6%
Malware 172
17.2%
DDoS 170
17.0%
Insider Threat 153
15.3%
SQL Injection 150
15.0%

Length

2024-02-26T20:27:49.729574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T20:27:49.854292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
phishing 179
13.7%
ransomware 176
13.5%
malware 172
13.2%
ddos 170
13.0%
insider 153
11.7%
threat 153
11.7%
sql 150
11.5%
injection 150
11.5%

Most occurring characters

ValueCountFrequency (%)
a 849
 
9.3%
n 808
 
8.8%
e 804
 
8.8%
i 661
 
7.2%
r 654
 
7.1%
h 511
 
5.6%
s 508
 
5.5%
o 496
 
5.4%
w 348
 
3.8%
D 340
 
3.7%
Other values (16) 3189
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6922
75.5%
Uppercase Letter 1943
 
21.2%
Space Separator 303
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 849
12.3%
n 808
11.7%
e 804
11.6%
i 661
9.5%
r 654
9.4%
h 511
7.4%
s 508
7.3%
o 496
7.2%
w 348
 
5.0%
t 303
 
4.4%
Other values (6) 980
14.2%
Uppercase Letter
ValueCountFrequency (%)
D 340
17.5%
S 320
16.5%
I 303
15.6%
P 179
9.2%
R 176
9.1%
M 172
8.9%
T 153
7.9%
Q 150
7.7%
L 150
7.7%
Space Separator
ValueCountFrequency (%)
303
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8865
96.7%
Common 303
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 849
 
9.6%
n 808
 
9.1%
e 804
 
9.1%
i 661
 
7.5%
r 654
 
7.4%
h 511
 
5.8%
s 508
 
5.7%
o 496
 
5.6%
w 348
 
3.9%
D 340
 
3.8%
Other values (15) 2886
32.6%
Common
ValueCountFrequency (%)
303
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 849
 
9.3%
n 808
 
8.8%
e 804
 
8.8%
i 661
 
7.2%
r 654
 
7.1%
h 511
 
5.6%
s 508
 
5.5%
o 496
 
5.4%
w 348
 
3.8%
D 340
 
3.7%
Other values (16) 3189
34.8%

Severity
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
Low
257 
Critical
256 
Medium
247 
High
240 

Length

Max length8
Median length6
Mean length5.261
Min length3

Characters and Unicode

Total characters5261
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowMedium
3rd rowMedium
4th rowLow
5th rowCritical

Common Values

ValueCountFrequency (%)
Low 257
25.7%
Critical 256
25.6%
Medium 247
24.7%
High 240
24.0%

Length

2024-02-26T20:27:49.994507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T20:27:50.099904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
low 257
25.7%
critical 256
25.6%
medium 247
24.7%
high 240
24.0%

Most occurring characters

ValueCountFrequency (%)
i 999
19.0%
L 257
 
4.9%
w 257
 
4.9%
o 257
 
4.9%
c 256
 
4.9%
a 256
 
4.9%
l 256
 
4.9%
t 256
 
4.9%
r 256
 
4.9%
C 256
 
4.9%
Other values (8) 1955
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4261
81.0%
Uppercase Letter 1000
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 999
23.4%
w 257
 
6.0%
o 257
 
6.0%
c 256
 
6.0%
a 256
 
6.0%
l 256
 
6.0%
t 256
 
6.0%
r 256
 
6.0%
e 247
 
5.8%
d 247
 
5.8%
Other values (4) 974
22.9%
Uppercase Letter
ValueCountFrequency (%)
L 257
25.7%
C 256
25.6%
M 247
24.7%
H 240
24.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5261
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 999
19.0%
L 257
 
4.9%
w 257
 
4.9%
o 257
 
4.9%
c 256
 
4.9%
a 256
 
4.9%
l 256
 
4.9%
t 256
 
4.9%
r 256
 
4.9%
C 256
 
4.9%
Other values (8) 1955
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 999
19.0%
L 257
 
4.9%
w 257
 
4.9%
o 257
 
4.9%
c 256
 
4.9%
a 256
 
4.9%
l 256
 
4.9%
t 256
 
4.9%
r 256
 
4.9%
C 256
 
4.9%
Other values (8) 1955
37.2%

Attempt Count
Real number (ℝ)

Distinct19
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.025
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-26T20:27:50.208490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5107843
Coefficient of variation (CV)0.54970417
Kurtosis-1.1811198
Mean10.025
Median Absolute Deviation (MAD)5
Skewness0.0032340051
Sum10025
Variance30.368744
MonotonicityNot monotonic
2024-02-26T20:27:50.336198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 63
 
6.3%
16 62
 
6.2%
10 60
 
6.0%
8 58
 
5.8%
6 57
 
5.7%
9 56
 
5.6%
2 56
 
5.6%
12 56
 
5.6%
11 55
 
5.5%
1 55
 
5.5%
Other values (9) 422
42.2%
ValueCountFrequency (%)
1 55
5.5%
2 56
5.6%
3 53
5.3%
4 47
4.7%
5 43
4.3%
6 57
5.7%
7 46
4.6%
8 58
5.8%
9 56
5.6%
10 60
6.0%
ValueCountFrequency (%)
19 63
6.3%
18 45
4.5%
17 54
5.4%
16 62
6.2%
15 40
4.0%
14 48
4.8%
13 46
4.6%
12 56
5.6%
11 55
5.5%
10 60
6.0%

Data Volume (MB)
Real number (ℝ)

Distinct628
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.045
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-02-26T20:27:50.482243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile50
Q1268.75
median504.5
Q3728
95-th percentile954.1
Maximum999
Range997
Interquartile range (IQR)459.25

Descriptive statistics

Standard deviation282.69606
Coefficient of variation (CV)0.56647408
Kurtosis-1.0870347
Mean499.045
Median Absolute Deviation (MAD)230.5
Skewness0.011353094
Sum499045
Variance79917.062
MonotonicityNot monotonic
2024-02-26T20:27:50.641808image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
611 6
 
0.6%
556 6
 
0.6%
328 6
 
0.6%
805 5
 
0.5%
34 5
 
0.5%
537 5
 
0.5%
28 4
 
0.4%
287 4
 
0.4%
57 4
 
0.4%
534 4
 
0.4%
Other values (618) 951
95.1%
ValueCountFrequency (%)
2 1
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 1
 
0.1%
13 1
 
0.1%
15 2
0.2%
16 3
0.3%
17 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
999 1
 
0.1%
998 1
 
0.1%
997 4
0.4%
996 2
0.2%
994 2
0.2%
992 2
0.2%
990 2
0.2%
989 2
0.2%
988 2
0.2%
987 1
 
0.1%

Source Latitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.056999
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative188
Negative (%)18.8%
Memory size7.9 KiB
2024-02-26T20:27:50.766745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation30.730582
Coefficient of variation (CV)1.0575966
Kurtosis0.36640377
Mean29.056999
Median Absolute Deviation (MAD)6.6606
Skewness-1.4332521
Sum29056.999
Variance944.3687
MonotonicityNot monotonic
2024-02-26T20:27:50.890605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
53.4808 76
 
7.6%
45.5017 75
 
7.5%
35.0116 71
 
7.1%
41.8781 70
 
7.0%
49.2827 70
 
7.0%
34.0522 68
 
6.8%
35.6895 67
 
6.7%
43.651 67
 
6.7%
-33.8688 65
 
6.5%
40.7128 65
 
6.5%
Other values (5) 306
30.6%
ValueCountFrequency (%)
-37.8136 63
6.3%
-33.8688 65
6.5%
-27.4698 60
6.0%
34.0522 68
6.8%
34.6937 61
6.1%
35.0116 71
7.1%
35.6895 67
6.7%
40.7128 65
6.5%
41.8781 70
7.0%
43.651 67
6.7%
ValueCountFrequency (%)
55.9533 60
6.0%
53.4808 76
7.6%
51.5074 62
6.2%
49.2827 70
7.0%
45.5017 75
7.5%
43.651 67
6.7%
41.8781 70
7.0%
40.7128 65
6.5%
35.6895 67
6.7%
35.0116 71
7.1%

Source Longitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.600353
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative613
Negative (%)61.3%
Memory size7.9 KiB
2024-02-26T20:27:50.989670image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation106.87011
Coefficient of variation (CV)6.4378216
Kurtosis-1.6689795
Mean16.600353
Median Absolute Deviation (MAD)116.0011
Skewness0.15702059
Sum16600.353
Variance11421.221
MonotonicityNot monotonic
2024-02-26T20:27:51.103587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-2.2426 76
 
7.6%
-73.5673 75
 
7.5%
135.7681 71
 
7.1%
-87.6298 70
 
7.0%
-123.1207 70
 
7.0%
-118.2437 68
 
6.8%
139.6917 67
 
6.7%
-79.347 67
 
6.7%
151.2093 65
 
6.5%
-74.006 65
 
6.5%
Other values (5) 306
30.6%
ValueCountFrequency (%)
-123.1207 70
7.0%
-118.2437 68
6.8%
-87.6298 70
7.0%
-79.347 67
6.7%
-74.006 65
6.5%
-73.5673 75
7.5%
-3.1883 60
6.0%
-2.2426 76
7.6%
-0.1278 62
6.2%
135.5023 61
6.1%
ValueCountFrequency (%)
153.0251 60
6.0%
151.2093 65
6.5%
144.9631 63
6.3%
139.6917 67
6.7%
135.7681 71
7.1%
135.5023 61
6.1%
-0.1278 62
6.2%
-2.2426 76
7.6%
-3.1883 60
6.0%
-73.5673 75
7.5%

Destination Latitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.914647
Minimum-37.8136
Maximum55.9533
Zeros0
Zeros (%)0.0%
Negative191
Negative (%)19.1%
Memory size7.9 KiB
2024-02-26T20:27:51.217163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-37.8136
Q134.0522
median40.7128
Q349.2827
95-th percentile55.9533
Maximum55.9533
Range93.7669
Interquartile range (IQR)15.2305

Descriptive statistics

Standard deviation31.117798
Coefficient of variation (CV)1.076195
Kurtosis0.29773786
Mean28.914647
Median Absolute Deviation (MAD)6.6606
Skewness-1.4114203
Sum28914.647
Variance968.31737
MonotonicityNot monotonic
2024-02-26T20:27:51.505013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
-37.8136 75
 
7.5%
49.2827 74
 
7.4%
43.651 73
 
7.3%
55.9533 72
 
7.2%
53.4808 72
 
7.2%
35.6895 70
 
7.0%
41.8781 68
 
6.8%
35.0116 67
 
6.7%
45.5017 66
 
6.6%
40.7128 66
 
6.6%
Other values (5) 297
29.7%
ValueCountFrequency (%)
-37.8136 75
7.5%
-33.8688 58
5.8%
-27.4698 58
5.8%
34.0522 64
6.4%
34.6937 59
5.9%
35.0116 67
6.7%
35.6895 70
7.0%
40.7128 66
6.6%
41.8781 68
6.8%
43.651 73
7.3%
ValueCountFrequency (%)
55.9533 72
7.2%
53.4808 72
7.2%
51.5074 58
5.8%
49.2827 74
7.4%
45.5017 66
6.6%
43.651 73
7.3%
41.8781 68
6.8%
40.7128 66
6.6%
35.6895 70
7.0%
35.0116 67
6.7%

Destination Longitude
Real number (ℝ)

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.819384
Minimum-123.1207
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative613
Negative (%)61.3%
Memory size7.9 KiB
2024-02-26T20:27:51.603926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-123.1207
5-th percentile-123.1207
Q1-79.347
median-2.2426
Q3139.6917
95-th percentile153.0251
Maximum153.0251
Range276.1458
Interquartile range (IQR)219.0387

Descriptive statistics

Standard deviation106.72976
Coefficient of variation (CV)6.3456404
Kurtosis-1.6655442
Mean16.819384
Median Absolute Deviation (MAD)116.0011
Skewness0.1517697
Sum16819.384
Variance11391.242
MonotonicityNot monotonic
2024-02-26T20:27:51.724830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
144.9631 75
 
7.5%
-123.1207 74
 
7.4%
-79.347 73
 
7.3%
-3.1883 72
 
7.2%
-2.2426 72
 
7.2%
139.6917 70
 
7.0%
-87.6298 68
 
6.8%
135.7681 67
 
6.7%
-73.5673 66
 
6.6%
-74.006 66
 
6.6%
Other values (5) 297
29.7%
ValueCountFrequency (%)
-123.1207 74
7.4%
-118.2437 64
6.4%
-87.6298 68
6.8%
-79.347 73
7.3%
-74.006 66
6.6%
-73.5673 66
6.6%
-3.1883 72
7.2%
-2.2426 72
7.2%
-0.1278 58
5.8%
135.5023 59
5.9%
ValueCountFrequency (%)
153.0251 58
5.8%
151.2093 58
5.8%
144.9631 75
7.5%
139.6917 70
7.0%
135.7681 67
6.7%
135.5023 59
5.9%
-0.1278 58
5.8%
-2.2426 72
7.2%
-3.1883 72
7.2%
-73.5673 66
6.6%

Interactions

2024-02-26T20:27:47.237734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.106586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.711480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.452536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.069104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.645824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.337183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.218722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.812129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.551966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.168199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.745968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.447029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.315867image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.905826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.644157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.264907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.843536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.542068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.414014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.002281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.750411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.363650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.944493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.639826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.511740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.222228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.845440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.455386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.044104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.738149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:44.612050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.359598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:45.939254image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:46.553375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-02-26T20:27:47.139076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-02-26T20:27:47.885407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T20:27:48.022570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
02024-01-12 00:00:003.74.124.5561.26.104.61MalwareLow9208-27.4698153.025134.0522-118.2437
12024-01-12 01:00:00113.188.61.22327.97.112.90RansomwareMedium1420-27.4698153.025135.6895139.6917
22024-01-12 02:00:00175.61.133.2671.111.137.21DDoSMedium16955.9533-3.1883-37.8136144.9631
32024-01-12 03:00:00216.23.15.2010.79.132.72MalwareLow539135.6895139.691745.5017-73.5673
42024-01-12 04:00:00169.243.204.167165.162.181.179RansomwareCritical1089935.6895139.691734.0522-118.2437
52024-01-12 05:00:00179.83.244.19421.105.176.221MalwareHigh260543.6510-79.347055.9533-3.1883
62024-01-12 06:00:0063.245.16.96117.17.79.208PhishingHigh47834.0522-118.243749.2827-123.1207
72024-01-12 07:00:00141.42.27.4838.17.41.9Insider ThreatMedium11393-27.4698153.025145.5017-73.5673
82024-01-12 08:00:0079.86.115.24596.228.66.129DDoSLow367451.5074-0.127849.2827-123.1207
92024-01-12 09:00:00213.45.252.12324.154.145.32SQL InjectionCritical116253.4808-2.242651.5074-0.1278
TimestampSource IPDestination IPAttack TypeSeverityAttempt CountData Volume (MB)Source LatitudeSource LongitudeDestination LatitudeDestination Longitude
9902024-02-22 06:00:00125.118.204.8616.249.141.224PhishingLow295655.9533-3.188345.5017-73.5673
9912024-02-22 07:00:00118.208.95.21535.86.255.146SQL InjectionCritical666749.2827-123.1207-33.8688151.2093
9922024-02-22 08:00:0040.12.234.12185.173.96.1MalwareMedium732743.6510-79.347035.0116135.7681
9932024-02-22 09:00:00200.204.189.166177.48.196.24SQL InjectionCritical10860-27.4698153.025143.6510-79.3470
9942024-02-22 10:00:00153.68.42.4151.230.127.99PhishingLow1541443.6510-79.347035.0116135.7681
9952024-02-22 11:00:0048.28.162.28.232.209.171DDoSHigh139635.6895139.691740.7128-74.0060
9962024-02-22 12:00:003.198.51.233162.172.242.232DDoSMedium1928-27.4698153.0251-37.8136144.9631
9972024-02-22 13:00:00161.158.193.43176.174.31.59SQL InjectionLow16543-33.8688151.209334.6937135.5023
9982024-02-22 14:00:00136.179.223.583.222.197.43DDoSHigh6940-27.4698153.025155.9533-3.1883
9992024-02-22 15:00:00183.35.26.20912.188.131.254PhishingMedium254751.5074-0.127843.6510-79.3470